Translational image alignment is a fundamental and commonly used preprocessing step in many medical imaging operations, such as image registration, image fusion, multiframe imaging, etc. In many applications, it can be crucial that the alignment algorithm is fast and robust to noise. The problem of image alignment becomes even more challenging when there are small deformations present in the images (for example, deformations due to patient breathing and organ movement) or when different types of imaging modalities produce the two images being registered. In such cases, intensity-based similarity measures can exhibit non-convex behavior, which renders the problem difficult for optimization. An example of such difficulties is illustrated in of FIG. 1, which shows a graph 100 depicting values of a cross correlation similarity measure as a function of the translational shift along the patient axis for a pair of images of the pelvis area of a patient's body. The graph 100 of FIG. 1 shows, among other things, how the presence of local maxima can cause difficulties in solving for the global maximum with gradient based optimization approaches.